id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_bghb2jlqpbf6lnr4hpzevghqwq	Seiki Ubukata	A unified approach for cluster-wise and general noise rejection approaches for k-means clustering	2019	20	.pdf	application/pdf	7306	1042	70	and CNR in HCM, we propose linear function threshold-based C-means (LiFTCM) Keywords Clustering, k-means, Noise rejection, Rough set theory For each cluster, objects distant from its center are rejected as noise. For each cluster, objects distant from its center are rejected as noise. West, 2004) as a rough-set-based C-means clustering, and Peters proposed a refined linear function threshold-based C-means (LiFTCM) by relaxing GRCM. the linear function threshold-based assignment in relaxed GRCM can realize GNR, CNR, linear function threshold-based object-cluster assignment in the proposed LiFTCM. this type of CNR, noise rejection is performed for each cluster by rejecting objects over δc Similar to HCM and GNR, in CNR, the cluster centers are calculated only using the Table 1 Relationship between HCM, GNR, CNR, and rough clustering, and their combinations in noise rejection (CNR) in hard C-means (HCM), we proposed linear function thresholdbased C-means (LiFTCM) by relaxing generalized rough C-means (GRCM) clustering.	./cache/work_bghb2jlqpbf6lnr4hpzevghqwq.pdf	./txt/work_bghb2jlqpbf6lnr4hpzevghqwq.txt